AI-Empowered Decision Support for COVID-19 Social Distancing

Authors

  • Hongchao Jiang Alibaba-NTU Singapore Joint Research Institute (JRI), Nanyang Technological University (NTU), Singapore
  • Wei Yang Bryan Lim Alibaba-NTU Singapore Joint Research Institute (JRI), Nanyang Technological University (NTU), Singapore
  • Jer Shyuan Ng Alibaba-NTU Singapore Joint Research Institute (JRI), Nanyang Technological University (NTU), Singapore
  • Harold Ze Chie Teng School of Computer Science and Engineering (SCSE), Nanyang Technological University (NTU), Singapore
  • Han Yu School of Computer Science and Engineering (SCSE), Nanyang Technological University (NTU), Singapore
  • Zehui Xiong Alibaba-NTU Singapore Joint Research Institute (JRI), Nanyang Technological University (NTU), Singapore
  • Dusit Niyato School of Computer Science and Engineering (SCSE), Nanyang Technological University (NTU), Singapore
  • Chunyan Miao Alibaba-NTU Singapore Joint Research Institute (JRI), Nanyang Technological University (NTU), Singapore School of Computer Science and Engineering (SCSE), Nanyang Technological University (NTU), Singapore

DOI:

https://doi.org/10.1609/aaai.v35i18.18007

Keywords:

COVID-19, Crowdsourcing, Unmanned Aerial Vehicle, Decision Support System

Abstract

The COVID-19 pandemic is one of the most severe challenges the world faces today. In order to contain the transmission of COVID-19, people around the world have been advised to practise social distancing. However, maintaining social distance is a challenging problem, as we often do not know beforehand how crowded the places we intend to visit are. In this paper, we demonstrate crowded.sg, an AI-empowered platform that leverages on Unmanned Aerial Vehicles (UAVs), crowdsourced images, and computer vision techniques to provide social distancing decision support.
AAAI-21 Proceedings Cover

Downloads

Published

2021-05-18

How to Cite

Jiang, H., Lim, W. Y. B., Ng, J. S., Teng, H. Z. C., Yu, H., Xiong, Z., Niyato, D., & Miao, C. (2021). AI-Empowered Decision Support for COVID-19 Social Distancing. Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 16044-16047. https://doi.org/10.1609/aaai.v35i18.18007

Issue

Vol. 35 No. 18: AAAI-21 Student Papers and Demonstrations

Section

AAAI Demonstration Track